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A new texture descriptor based on local micro-pattern for detection of architectural distortion in mammographic images
Author(s): Helder C. R. de Oliveira; Diego R. Moraes; Gustavo A. Reche; Lucas R. Borges; Juliana H. Catani; Nestor de Barros; Carlos F. E. Melo; Adilson Gonzaga; Marcelo A. C. Vieira
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Paper Abstract

This paper presents a new local micro-pattern texture descriptor for the detection of Architectural Distortion (AD) in digital mammography images. AD is a subtle contraction of breast parenchyma that may represent an early sign of breast cancer. Due to its subtlety and variability, AD is more difficult to detect compared to microcalcifications and masses, and is commonly found in retrospective evaluations of false-negative mammograms. Several computer-based systems have been proposed for automatic detection of AD, but their performance are still unsatisfactory. The proposed descriptor, Local Mapped Pattern (LMP), is a generalization of the Local Binary Pattern (LBP), which is considered one of the most powerful feature descriptor for texture classification in digital images. Compared to LBP, the LMP descriptor captures more effectively the minor differences between the local image pixels. Moreover, LMP is a parametric model which can be optimized for the desired application. In our work, the LMP performance was compared to the LBP and four Haralick's texture descriptors for the classification of 400 regions of interest (ROIs) extracted from clinical mammograms. ROIs were selected and divided into four classes: AD, normal tissue, microcalcifications and masses. Feature vectors were used as input to a multilayer perceptron neural network, with a single hidden layer. Results showed that LMP is a good descriptor to distinguish AD from other anomalies in digital mammography. LMP performance was slightly better than the LBP and comparable to Haralick's descriptors (mean classification accuracy = 83%).

Paper Details

Date Published: 3 March 2017
PDF: 9 pages
Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101342U (3 March 2017); doi: 10.1117/12.2255516
Show Author Affiliations
Helder C. R. de Oliveira, Univ. of São Paulo (Brazil)
Diego R. Moraes, Univ. of São Paulo (Brazil)
Gustavo A. Reche, Univ. of São Paulo (Brazil)
Lucas R. Borges, Univ. of São Paulo (Brazil)
Juliana H. Catani, Univ. of São Paulo (Brazil)
Nestor de Barros, Univ. of São Paulo (Brazil)
Carlos F. E. Melo, Clínica Eco & Mama Diagnóstico Digital (Brazil)
Adilson Gonzaga, Univ. of São Paulo (Brazil)
Marcelo A. C. Vieira, Univ. of São Paulo (Brazil)


Published in SPIE Proceedings Vol. 10134:
Medical Imaging 2017: Computer-Aided Diagnosis
Samuel G. Armato; Nicholas A. Petrick, Editor(s)

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